3D Object Recognition by Surface Registration of Interest Segments
Why this work is in the frame
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Bibliographic record
Abstract
An object recognition system Based on registering repeatable interest segments from 3D surfaces is presented. The strength of this approach lies in its independence of local features, which can be unreliable when corrupted by noise, and indistinct for certain objects and surfaces. The proposed framework is Based on recent advances in segmenting 3D data into repeatable interest segments, followed by efficient surface registration of model and scene segments, where pose clustering returns the best pose candidates. A quality measure Based on reprojection of the model points and pose refinement are then used to select the best pose. The proposed method is demonstrated experimentally to be both accurate and robust when tested against a variety of partially occluded free-form objects in cluttered scenes, achieving an average accuracy of 93% on an accurate and high resolution LiDAR data set, and 81% on a noisy and low resolution Kinect data set.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it